Reduced insulin sensitivity or insulin resistance (IR) is a forerunner of Type 2 diabetes (T2D). Differences in the prevalence of IR among ethnic groups suggest its genetic etiology. However, recent studies implementing genetic association approaches were only nominally successful in defining the genetically-regulated molecular and cellular mechanisms of IR. Our physiologic and genomic studies suggest that (a) IR results from derangement in expression of thousands of genes in tissues involved in glucose homeostasis, and (b) genetic variants, such as single nucleotide polymorphisms (SNPs), determine the expression level of a subset of IR-associated genes. Thus, transcript levels are key molecular phenotypes associated with IR and are proximal to the action of genetic variants. A subset of genetically-regulated transcript subnetworks that operate within the large highly interconnected global expression networks in disease-relevant tissues can cause IR, but remain poorly understood. We hypothesize that, regulatory SNPs in expression quantitative trait loci (eQTLs) determine transcript levels of key driver genes, configure the expression subnetworks in adipose and muscle tissue, and are causal determinants for IR. The genetic architecture of eQTLs determines the heterogeneity in causal mechanisms of IR. Our preliminary data support the concept of causal genetic regulators of subnetworks in modulating insulin sensitivity. Challenging the current paradigms, our Aim 1 is to implement our cutting-edge Multiscale Network Modelling Approach to integrate measures of glucose homeostasis (SI and Matsuda index from FSIVGT and OGTT, respectively); adipose and muscle tissue transcript profiles; eSNP data from African American participants in the AAGMEx cohort (N=260); and genetic and epigenetic regulation data from knowledge bases to discover subnetworks and genetic drivers that are causally linked to IR. In Aim 2, we will validate insulin sensitivity-associated genetically-regulated subnetworks, and determine common and ethnically-predominant genetic regulatory mechanisms of IR, using multi-omics data from similarly phenotyped European ancestry individuals from the METSIM (N=770) and AREA (N=99) cohorts. These results will be compared with those from the AAGMEx cohort. Key driver genes of insulin sensitivity-associated genetically regulated adipose and muscle tissue subnetworks from Aims 1-2 will then be prioritized based on statistical ranking to validate their regulatory roles. In Aim 3, we will focus on understanding molecular and cellular mechanisms modulated by 10 selected putative key regulatory genes. Using in vitro genetic perturbation experiments in relevant human cell models we have already established, we will modulate the expression of these genes, identify target pathways, and examine IR-determining cellular mechanisms (e.g. metabolism, signal transduction, differentiation, inflammation, cell-cell interaction) regulated by these genes. Our study will be among the first to define the genetic regulatory networks of IR, a key step towards development of novel therapeutic options for prevention of IR and subsequent T2D.